Robust automatic segmentation of various anatomical structures in a medical image is a key enabler in improving clinical workflows. Here, the term segmentation refers to the identification of the anatomical structure in the medical image by, e.g., delineation of the boundaries of the anatomical structure, by labeling of the voxels enclosed by the boundaries, etc. Once such segmentation has been performed, it is possible to extract clinical parameters such as, in case of, e.g., a cardiac structure, ventricular mass, ejection fraction and wall thickness. Consequently, automatic segmentation can significantly reduce the scan-to-diagnosis time, and thus help clinicians in establishing more efficient patient management.
It is known to segment an anatomical structure in a medical image using a deformable surface model. Such type of segmentation is also referred to as model-based segmentation. The deformable model may be defined by model data. The model data may define a geometry of the anatomical structure, e.g., in the form of a multi-compartmental mesh of triangles. Inter-patient and inter-disease-stage shape variability may be modeled by assigning an affine transformation to each part of such a deformable model. Affine transformations cover translation, rotation, scaling along different coordinate axes and shearing. Moreover, mesh regularity may be maintained by interpolation of the affine transformations at the transitions between different parts of the deformable model.
The applying of a deformable model to the image data of the medical image may involve the use of an adaptation technique, also termed ‘mesh adaptation’ in case of a mesh-based model. For example, the adaptation technique may comprise optimizing an energy function based on an external energy term which adapts the deformable model to the image data and an internal energy term which maintains a rigidness of the deformable model.
Deformable models of the above type, as well as other types, are known per se, as are adaptation techniques for the applying of such models to a medical image.
For example, a publication titled “Automatic Model-based Segmentation of the Heart in CT Images” by O. Ecabert et al., IEEE Transactions on Medical Imaging 2008, 27(9), pp. 1189-1201, describes a model-based approach for the automatic segmentation of the heart (four chambers, myocardium, and great vessels) from three-dimensional (3D) Computed Tomography (CT) images. Here, model adaptation is performed progressively increasing the degrees-of-freedom of the allowed deformations to improve convergence as well as segmentation accuracy. The heart is first localized in the image using a 3D implementation of the generalized Hough transform. Pose misalignment is corrected by matching the model to the image making use of a global similarity transformation. The complex initialization of the multi-compartment mesh is then addressed by assigning an affine transformation to each anatomical region of the model. Finally, a deformable adaptation is performed to accurately match the boundaries of the patient's anatomy.